You want to predict whether a Euro banknote is forged or genuine. For this purpose, you collected a random sample of 900 Euro banknotes and took their images. Then you applied Wavelet Transform tools to extract the following features from each image: VARIANCE, SKEWNESS, KURTOSIS and ENTROPY. The first six lines of the data are shown as follows: 1 234 5 6 GENUINE yes yes yes 3.821300 no 0.704900 VARIANCE SKEWNESS KURTOSIS 1.334900 6.11890 0.46497 5.042900 -0.52974 0.50439 1.10600 0.23175 2.01330 2.05640 0.17174 -1.78590 0.36119 9.74840 0.15394 -1.61340 yes -0.016103 yes -0.782890 11.36030 -0.37644 -7.04950 The variable GENUINE equals no for forged Euro banknotes, and yes otherwise. You used the data and built a Logistic Regression model where GENUINE is the dependent variable. The coefficient estimates of the model are given as follows: Coefficients: (Intercept) -5.26 4.96 VARIANCE SKEWNESS KURTOSIS ENTROPY Estimate 2.64 3.39 0.27 ENTROPY 0.49826 Consider the following Euro banknote: VARIANCE 4.39 SKEWNESS -2.12 CURTOSIS -3.79 ENTROPY -0.03 Based on the model, what percent of such banknotes are predicted to be forged? Enter the answer as a percentage with one decimal place (e.g. 21.4%). (If your answer is not correct to three decimal places but is still within Correct Answer10.80 Correct Answer, you will receive 50% partial credit. I